摘要
针对基于表面肌电信号(sEMG)的手势识别准确率低、计算量大的问题,提出一种基于混合空洞卷积神经网络组合双向门控循环单元与注意力机制(HDC-BiGRU-Attention)的表面肌电信号手势识别方法。相比普通CNN,HDC通过设置奇偶混合且大小不同的膨胀率,可以扩大感受野,减少过拟合,提取到更多特征。BiGRU模块能很好地提取和处理数据的时序特征,Attention模块为重要特征赋予更大的权重,可以提高准确率。在NinaproDB1数据集和自采数据集上分别实现92.72%和97.85%的准确率。
Aimed at the problem of low accuracy and large amount of calculation for gesture recognition based on surface electromyography(sEMG),a method for sEMG gesture recognition based on a hybrid dilated convolutional neural network combining bidirectional gated recurrent unit and attention mechanism is proposed.Compared with the ordinary CNN,HDC can expand the receptive field,reduce over-fitting,and extract more features by setting the dilation rate to parity hybrid and different sizes.The BiGRU module can extract and process the timing features of the data well,and attention module gives greater weight to important features,which can improve accuracy.The accuracy rates of 92.72%and 97.85%were achieved on the NinaproDB1 dataset and the self-acquisition dataset,respectively.
作者
张凯
陈峰
Zhang Kai;Chen Feng(College of Electrical Engineering,Nantong University,Nantong 226019,Jiangsu,China)
出处
《计算机应用与软件》
北大核心
2024年第11期220-227,共8页
Computer Applications and Software
基金
江苏省青年基金项目(BK20180953)。
